Search results for: stochastic gradient boosting

Authors: Aamna Lawrence, Ashutosh Mishra

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Tremors occur in 60% of the patients who have Multiple Sclerosis (MS), the most common demyelinating disease that affects the central and peripheral nervous system, and are the primary cause of disability in young adults. While pharmacological agents provide minimal benefits, surgical interventions like Deep Brain Stimulation and Thalamotomy are riddled with dangerous complications which make non-invasive electrical stimulation an appealing treatment of choice for dealing with tremors. Hence, we hypothesized that if the non-invasive electrical stimulation parameters (mainly frequency) can be computed by mathematically modeling the nerve fibre to take into consideration the minutest details of the axon morphologies, tremors due to demyelination can be optimally alleviated. In this computational study, we have modeled the random demyelination pattern in a nerve fibre that typically manifests in MS using the High-Density Hodgkin-Huxley model with suitable modifications to account for the myelin. The internode of the nerve fibre in our model could have up to ten demyelinated regions each having random length and myelin thickness. The arrival time of action potentials traveling the demyelinated and the normally myelinated nerve fibre between two fixed points in space was noted, and its relationship with the nerve fibre radius ranging from 5µm to 12µm was analyzed. It was interesting to note that there were no overlaps between the arrival time for action potentials traversing the demyelinated and normally myelinated nerve fibres even when a single internode of the nerve fibre was demyelinated. The study gave us an opportunity to design DC pulses whose frequency of application would be a function of the random demyelination pattern to block only the delayed tremor-causing action potentials. The DC pulses could be delivered to the peripheral nervous system non-invasively by an electrode bracelet that would suppress any shakiness beyond it thus paving the way for wearable neuro-rehabilitative technologies.

Keywords: demyelination, Hodgkin-Huxley model, non-invasive electrical stimulation, tremor

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Authors: Pragathi Gurumurthy, Christina Hagen, Patricia Ulloa, Martin A. Koch, Thorsten M. Buzug

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Obstructive sleep apnea (OSA) is a sleep disorder where the patient suffers a disturbed airflow during sleep due to partial or complete occlusion of the pharyngeal airway. Recently, numerical simulations have been used to better understand the mechanism of pharyngeal collapse. However, to gain confidence in the solutions so obtained, an experimental validation is required. Therefore, in this study an experimental validation of computational fluid dynamics (CFD) used for the study of human pharyngeal flow patterns during OSA is performed. A stationary incompressible Navier-Stokes equation solved using the finite element method was used to numerically study the flow patterns in a computed tomography-based human pharynx model. The inlet flow rate was set to 250 ml/s and such that a flat profile was maintained at the inlet. The outlet pressure was set to 0 Pa. The experimental technique used for the validation of CFD of fluid flow patterns is phase contrast-MRI (PC-MRI). Using the same computed tomography data of the human pharynx as in the simulations, a phantom for the experiment was 3 D printed. Glycerol (55.27% weight) in water was used as a test fluid at 25°C. Inflow conditions similar to the CFD study were simulated using an MRI compatible flow pump (CardioFlow-5000MR, Shelley Medical Imaging Technologies). The entire experiment was done on a 3 T MR system (Ingenia, Philips) with 108 channel body coil using an RF-spoiled, gradient echo sequence. A comparison of the axial velocity obtained in the pharynx from the numerical simulations and PC-MRI shows good agreement. The region of jet impingement and recirculation also coincide, therefore validating the numerical simulations. Hence, the experimental validation proves the reliability and correctness of the numerical simulations.

Keywords: computational fluid dynamics, experimental validation, phase contrast-MRI, obstructive sleep apnea

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Authors: O. Ubani, H. I. Atagana, M. S. Thantsha

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This study was conducted to measure the reduction in polycyclic aromatic hydrocarbons (PAHs) content in oil sludge by co-composting the sludge with pig, cow, horse and poultry manures under laboratory conditions. Four kilograms of soil spiked with 800 g of oil sludge was co-composted differently with each manure in a ratio of 2:1 (w/w) spiked soil:manure and wood-chips in a ratio of 2:1 (w/v) spiked soil:wood-chips. Control was set up similar as the one above but without manure. Mixtures were incubated for 10 months at room temperature. Compost piles were turned weekly and moisture level was maintained at between 50% and 70%. Moisture level, pH, temperature, CO2 evolution and oxygen consumption were measured monthly and the ash content at the end of experimentation. Bacteria capable of utilizing PAHs were isolated, purified and characterized by molecular techniques using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), amplification of the 16S rDNA gene using the specific primers (16S-P1 PCR and 16S-P2 PCR) and the amplicons were sequenced. Extent of reduction of PAHs was measured using automated soxhlet extractor with dichloromethane as the extraction solvent coupled with gas chromatography/mass spectrometry (GC/MS). Temperature did not exceed 27.5O°C in all compost heaps, pH ranged from 5.5 to 7.8 and CO2 evolution was highest in poultry manure at 18.78 µg/dwt/day. Microbial growth and activities were enhanced. Bacteria identified were Bacillus, Arthrobacter and Staphylococcus species. Results from PAH measurements showed reduction between 77 and 99%. The results from the control experiments may be because it was invaded by fungi. Co-composting of spiked soils with animal manures enhanced the reduction in PAHs. Interestingly, all bacteria isolated and identified in this study were present in all treatments, including the control.

Keywords: bioremediation, co-composting, oil refinery sludge, PAHs, bacteria spp, animal manures, molecular techniques

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Authors: Long Long Chen, Xinwei Liao, Shanfa Tang, Shaojing Jiang, Ruijia Tang, Rui Wang, Shu Yun Feng, Si Yao Wang

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Tight oil reservoirs have poor physical properties, insufficient formation energy, and low natural productivity; it is necessary to effectively improve their crude oil recovery. CO₂ flooding is an important technical means to enhance oil recovery and achieve effective CO₂ storage in tight oil reservoirs, but its heterogeneity is strong, which makes CO₂ flooding prone to gas channeling and poor recovery. Aiming at the problem of gas injection channeling, combined with the excellent performance of low interfacial tension viscoelastic fluid (GOBTK), the research on CO₂-low interfacial tension viscoelastic fluid synergistic oil displacement in tight reservoirs was carried out, and the synergy of CO₂ and low interfacial tension viscoelastic fluid was discussed. Oil displacement mechanism. Experiments show that GOBTK has good injectability in tight oil reservoirs (Kg=0.141～0.793mD); CO₂-0.4% GOBTK synergistic flooding can improve the recovery factor of low permeability layers (31.41%) under heterogeneous (gradient difference of 10) conditions the) effect is better than that of CO₂ flooding (0.56%) and 0.4% GOBT-water flooding (20.99%); CO₂-GOBT synergistic oil displacement mechanism includes: 1) The formation of CO₂ foam increases the flow resistance of viscoelastic fluid, forcing the displacement fluid to flow 2) GOBTK can emulsify and disperse residual oil into small oil droplets, and smoothly pass through narrow pores to produce; 3) CO₂ dissolved in GOBTK synergistically enhances the water wettability of the core, and the use of viscosity Elastomeric fluid injection and stripping of residual oil; 4) CO₂-GOBTK synergy superimposes multiple mechanisms, effectively improving the swept volume and oil washing efficiency of the injected fluid to the reservoir.

Keywords: tight oil reservoir, CO₂ flooding, low interfacial tension viscoelastic fluid flooding, synergistic oil displacement, EOR mechanism

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Authors: Ayten Bakhtiyar Khasayeva

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Lower Kura depression is a part of the South Caspian Basin (SCB), located between the folded regions of the Greater and Lesser Caucasus. The region is characterized by thick sedimentary cover 22 km (SCB up to 30 km), high sedimentation rate, low geothermal gradient (average value corresponds to 2 °C / 100m). There is Quaternary, Pliocene, Miocene and Oligocene deposits take part in geological structure. Miocene and Oligocene deposits are opened by prospecting and exploratory wells in the areas of Kalamaddin and Garabagli. There are 25 mud volcanoes within the territory of the Lower Kura depression, which are the unique source of information about hydrocarbons contenting great depths. During the wells data research, solid erupted products and mud volcano fluids, and according to the geological and thermal characteristics of the region, it was determined that the main phase of the hydrocarbon generation (MK1-AK2) corresponds to a wide range of depths from 10 to 14 km, which corresponds to the Pliocene-Miocene sediments, and to the "oil and gas windows" according to the intended meaning of R0 ≈ 0,65-0,85%. Fluids of mud volcanoes comprise by the following phases - gas, water. Gas phase consists mainly of methane (99%) of heavy hydrocarbons (С2+ hydrocarbons), CO2, N2, inert components He, Ar. The content of the С2+ hydrocarbons in the gases of mud volcanoes associated with oil deposits is increased. Carbon isotopic composition of methane for the Lower Kura depression varies from -40 ‰ to -60 ‰. Water of mud volcanoes are represented by all four genetic types. However the most typical types of water are HCN type. According to the Mg-Li geothermometer formation of mud waters corresponds to the temperature range from 20 °C to 140 °C (PC2). The solid product emissions of mud volcanoes identified 90 minerals and 30 trace elements. As a result geochemical investigation, thermobaric and geological conditions, zone oil and gas generation - the prospect of the Lower Kura depression is projected to depths greater than 10 km.

Keywords: geology, geochemistry, mud volcanoes, petroleum potential

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Authors: Ozan Yavuz Baytemir, Ender Cigeroglu, Gokhan Osman Ozgen

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Vibration is an important issue in the design of various components of aerospace, marine and vehicular applications. In order not to lose the components’ function and operational performance, vibration isolation design involving the optimum isolator properties selection and isolator positioning processes appear to be a critical study. Knowing the growing need for the vibration isolation system design, this paper aims to present two types of software capable of implementing modal analysis, response analysis for both random and harmonic types of excitations, static deflection analysis, Monte Carlo simulations in addition to study of parameter and location optimization for different types of isolation problem scenarios. Investigating the literature, there is no such study developing a software-based tool that is capable of implementing all those analysis, simulation and optimization studies in one platform simultaneously. In this paper, the theoretical system model is generated for a 6-DOF rigid body. The vibration isolation system of any mechanical structure is able to be optimized using hybrid method involving both global search and gradient-based methods. Defining the optimization design variables, different types of optimization scenarios are listed in detail. Being aware of the need for a user friendly vibration isolation problem solver, two types of graphical user interfaces (GUIs) are prepared and verified using a commercial finite element analysis program, Ansys Workbench 14.0. Using the analysis and optimization capabilities of those GUIs, a real application used in an air-platform is also presented as a case study at the end of the paper.

Keywords: hybrid optimization, Monte Carlo simulation, multi-degree-of-freedom system, parameter optimization, location optimization, passive vibration isolation analysis

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Authors: Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević, Lidija R. Jevrić

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Prion is a protein substance whose certain form is considered as infectious agent. It is presumed to be the cause of the transmissible spongiform encephalopathies (TSEs). The protein it is composed of, called PrP, can fold in structurally distinct ways. At least one of those 3D structures is transmissible to other prion proteins. Prions can be found in brain tissue of healthy people and have certain biological role. The structure of prions naturally occurring in healthy organisms is marked as PrPc, and the structure of infectious prion is labeled as PrPSc. PrPc may play a role in synaptic plasticity and neuronal development. Also, it may be required for neuronal myelin sheath maintenance, including a role in iron uptake and iron homeostasis. PrPSc can be considered as an environmental pollutant. The main aim of this study was to carry out the molecular modeling and calculation of molecular descriptors (lipophilicity, physico-chemical and topological descriptors) of structurally diverse compounds which can be considered as anti-prion agents. Molecular modeling was conducted applying ChemBio3D Ultra version 12.0 software. The obtained 3D models were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. The Austin Model 1 (AM-1) was used for full geometry optimization of all structures. The obtained set of molecular descriptors is applied in analysis of similarities and dissimilarities among the tested compounds. This study is an important step in further development of quantitative structure-activity relationship (QSAR) models, which can be used for prediction of anti-prion activity of newly synthesized compounds.

Keywords: chemometrics, molecular modeling, molecular descriptors, prions, QSAR

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Authors: Julius M. Ndambuki, Gislar E. Kifanyi, Samuel N. Odai, Charles Gyamfi

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Conjunctive management of surface and groundwater resources is a challenging task due to the spatial and temporal variability nature of hydrology as well as hydrogeology of the water storage systems. Surface water-groundwater hydrogeology is highly uncertain; thus it is imperative that this uncertainty is explicitly accounted for, when managing water resources. Various methodologies have been developed and applied by researchers in an attempt to account for the uncertainty. For example, simulation-optimization models are often used for conjunctive water resources management. However, direct application of such an approach in which all realizations are considered at each iteration of the optimization process leads to a very expensive optimization in terms of computational time, particularly when the number of realizations is large. The aim of this paper, therefore, is to introduce and apply an efficient approach referred to as Retrospective Optimization Approximation (ROA) that can be used for optimizing conjunctive use of surface water and groundwater over a multiple hydrogeological model simulations. This work is based on stochastic simulation-optimization framework using a recently emerged technique of sample average approximation (SAA) which is a sampling based method implemented within the Retrospective Optimization Approximation (ROA) approach. The ROA approach solves and evaluates a sequence of generated optimization sub-problems in an increasing number of realizations (sample size). Response matrix technique was used for linking simulation model with optimization procedure. The k-means clustering sampling technique was used to map the realizations. The methodology is demonstrated through the application to a hypothetical example. In the example, the optimization sub-problems generated were solved and analysed using “Active-Set” core optimizer implemented under MATLAB 2014a environment. Through k-means clustering sampling technique, the ROA – Active Set procedure was able to arrive at a (nearly) converged maximum expected total optimal conjunctive water use withdrawal rate within a relatively few number of iterations (6 to 7 iterations). Results indicate that the ROA approach is a promising technique for optimizing conjunctive water use of surface water and groundwater withdrawal rates under hydrogeological uncertainty.

Keywords: conjunctive water management, retrospective optimization approximation approach, sample average approximation, uncertainty

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Authors: Narjes Abbasabadi

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As the world continues to urbanize and suburbanize, where suburbanization associated with mass sprawl has been the dominant form of this expansion, sustainable development challenges will be more concerned. Sprawling, characterized by low density and automobile dependency, presents significant environmental issues regarding energy consumption and Co2 emissions. This paper examines the vertical expansion of suburbs integrated into mass transit nodes as a planning strategy for boosting density, intensification of land use, conversion of single family homes to multifamily dwellings or mixed use buildings and development of viable alternative transportation choices. It analyzes the spatial patterns of tall building transit-oriented development (TB-TOD) of suburban regions in Sydney (Australia), Toronto (Canada), and Washington D.C. (United States). The main objectives of this research seek to understand the effect of the new morphology of suburban tall, the physical dimensions of individual buildings and their arrangement at a larger scale with energy efficiency. This study aims to answer these questions: 1) why and how can the potential phenomenon of vertical expansion or high-rise development be integrated into suburb settings? 2) How can this phenomenon contribute to an overall denser development of suburbs? 3) Which spatial pattern or typologies/ sub-typologies of the TB-TOD model do have the greatest energy efficiency? It addresses these questions by focusing on 1) energy, heat energy demand (excluding cooling and lighting) related to design issues at two levels: macro, urban scale and micro, individual buildings—physical dimension, height, morphology, spatial pattern of tall buildings and their relationship with each other and transport infrastructure; 2) Examining TB-TOD to provide more evidence of how the model works regarding ridership. The findings of the research show that the TB-TOD model can be identified as the most appropriate spatial patterns of tall buildings in suburban settings. And among the TB-TOD typologies/ sub-typologies, compact tall building blocks can be the most energy efficient one. This model is associated with much lower energy demands in buildings at the neighborhood level as well as lower transport needs in an urban scale while detached suburban high rise or low rise suburban housing will have the lowest energy efficiency. The research methodology is based on quantitative study through applying the available literature and static data as well as mapping and visual documentations of urban regions such as Google Earth, Microsoft Bing Bird View and Streetview. It will examine each suburb within each city through the satellite imagery and explore the typologies/ sub-typologies which are morphologically distinct. The study quantifies heat energy efficiency of different spatial patterns through simulation via GIS software.

Keywords: energy efficiency, spatial pattern, suburb, tall building transit-oriented development (TB-TOD)

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Authors: Jaypalsinh Parmar, Pintu Nakrani, Bhaumik Shah

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Digital Elevation Model (DEM) is elevation data of the virtual grid on the ground. DEM can be used in application in GIS such as hydrological modelling, flood forecasting, morphometrical analysis and surveying etc.. For morphometrical analysis the stream flow network plays a very important role. DEM lacks accuracy and cannot match field data as it should for accurate results of morphometrical analysis. The present study focuses on comparing the Agree method and the conventional Shortest path method for finding out morphometric parameters in the flat region of the Lower Tapi Basin which is located in the western India. For the present study, open source SRTM (Shuttle Radar Topography Mission with 1 arc resolution) and toposheets issued by Survey of India (SOI) were used to determine the morphometric linear aspect such as stream order, number of stream, stream length, bifurcation ratio, mean stream length, mean bifurcation ratio, stream length ratio, length of overland flow, constant of channel maintenance and aerial aspect such as drainage density, stream frequency, drainage texture, form factor, circularity ratio, elongation ratio, shape factor and relief aspect such as relief ratio, gradient ratio and basin relief for 53 catchments of Lower Tapi Basin. Stream network was digitized from the available toposheets. Agree DEM was created by using the SRTM and stream network from the toposheets. The results obtained were used to demonstrate a comparison between the two methods in the flat areas.

Keywords: agree method, morphometric analysis, lower Tapi basin, shortest path method

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Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: O.F. Akinmoladun, F.N. Fon, C.T. Mpendulo, O. Okoh

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The sustainability of livestock production is under threat as a result of water scarcity, fluctuating precipitation, and high environmental temperature. These combined stressors have impacted negatively on general animal production and welfare, necessitating a very reliable and cost-effective management practices, especially in arid and water-limited regions of the world. Instead of the above, this study was designed to investigate the growth performance and physiological response of water-restricted Xhosa ear-lobe goats fed diets supplemented with single or multiple vitamin C (VC) during summer. The total forty-eight goats used for the experiment were balanced for body weight and randomly assigned to the seven treatment groups (seven goats/treatment): GI (W100%); GII (W70%); GIII (W50%); GIV (W70%+3g/day VC); GV ((W50% +3g/day VC); GVI (W70%+3g/d VC+extra 5g on every eight-day); GVII (W50%+3g/d VC+extra 5g on every eight-day). The design was a complete randomized design and VC was administered per os. At the end of the 75-day feeding trial, GIII (W50%) animals were the most affected (P<0.05) and the effect was more pronounced in their body condition scores (BCs). Weight loss and depression in feed intake due to water restriction (P<0.05) were attenuated by VC treated groups (GIV-GVII). Changes in body thermal gradient (BTG) and rectal temperature (RcT) were similar (P>0.05) across the various experimental groups. The attenuation effect of VC was significant in responses to respiratory rate (RR) and cortisol. Supplementation of VC (either single or multiple) did not significantly (P>0.05) improve water restriction effect on body condition scores (BCs) and FAMACHA©. The current study found out that Xhosa ear lobe goats can adapt to the prevailing bioclimatic changes and limited water intake. However, supplementation of vitamin C can be beneficial at modulating these stressful stimuli. Continuous consistencies in the outcome of vitamin C on water-stressed animals can help validate recommendations especially to farmers in the arid and water-limited regions across the globe.

Keywords: vitamin C, Xhosa ear-lobe, thermotolerance, water stress

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Authors: Danjuma Bawa

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This paper aimed at exploring the capabilities of Location-Allocation model in complementing the strides of the existing physical planning models in the location and distribution of facilities for regional consumption. The paper was designed to provide a blueprint to the Nigerian government and other donor agencies especially the Fertilizer Distribution Initiative (FDI) by the federal government for the revitalization of the terrorism ravaged regions. Theoretical underpinnings of central place theory related to spatial distribution, interrelationships, and threshold prerequisites were reviewed. The study showcased how Location-Allocation Model (L-AM) alongside Central Place Theory (CPT) was applied in Geographic Information System (GIS) environment to; map and analyze the spatial distribution of settlements; exploit their physical and economic interrelationships, and to explore their hierarchical and opportunistic influences. The study was purely spatial qualitative research which largely used secondary data such as; spatial location and distribution of settlements, population figures of settlements, network of roads linking them and other landform features. These were sourced from government ministries and open source consortium. GIS was used as a tool for processing and analyzing such spatial features within the dictum of CPT and L-AM to produce a comprehensive spatial digital plan for equitable and judicious location and distribution of fertilizer deports in the study area in an optimal way. Population threshold was used as yardstick for selecting suitable settlements that could stand as service centers to other hinterlands; this was accomplished using the query syntax in ArcMapTM. ArcGISTM’ network analyst was used in conducting location-allocation analysis for apportioning of groups of settlements around such service centers within a given threshold distance. Most of the techniques and models ever used by utility planners have been centered on straight distance to settlements using Euclidean distances. Such models neglect impedance cutoffs and the routing capabilities of networks. CPT and L-AM take into consideration both the influential characteristics of settlements and their routing connectivity. The study was undertaken in two terrorism ravaged Local Government Areas of Adamawa state. Four (4) existing depots in the study area were identified. 20 more depots in 20 villages were proposed using suitability analysis. Out of the 300 settlements mapped in the study area about 280 of such settlements where optimally grouped and allocated to the selected service centers respectfully within 2km impedance cutoff. This study complements the giant strides by the federal government of Nigeria by providing a blueprint for ensuring proper distribution of these public goods in the spirit of bringing succor to these terrorism ravaged populace. This will ardently at the same time help in boosting agricultural activities thereby lowering food shortage and raising per capita income as espoused by the government.

Keywords: central place theory, GIS, location-allocation, network analysis, urban and regional planning, welfare economics

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Authors: Poonam Malik, Ravi Bhushan

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This work describes a very efficient approach for synthesis of activated ester of (S)-naproxen which was characterized by UV, IR, ¹HNMR, elemental analysis and polarimetric studies. It was used as a C-N bond forming chiral derivatizing reagent for further synthesis of diastereomeric amides of (RS)-salbutamol (a β₂ agonist that belongs to the group β-adrenolytic and is marketed as racamate) under microwave irradiation. The diastereomeric pair was separated by achiral phase HPLC, using mobile phase in gradient mode containing methanol and aqueous triethylaminephosphate (TEAP); separation conditions were optimized with respect to pH, flow rate, and buffer concentration and the method of separation was validated as per International Council for Harmonisation (ICH) guidelines. The reagent proved to be very effective for on-line sensitive detection of the diastereomers with very low limit of detection (LOD) values of 0.69 and 0.57 ng mL⁻¹ for diastereomeric derivatives of (S)- and (R)-salbutamol, respectively. The retention times were greatly reduced (2.7 min) with less consumption of organic solvents and large (α) as compared to literature reports. Besides, the diastereomeric derivatives were separated and isolated by preparative HPLC; these were characterized and were used as standard reference samples for recording ¹HNMR and IR spectra for determining absolute configuration and elution order; it ensured the success of diastereomeric synthesis and established the reliability of enantioseparation and eliminated the requirement of pure enantiomer of the analyte which is generally not available. The newly developed reagent can suitably be applied to several other amino group containing compounds either from organic syntheses or pharmaceutical industries because the presence of (S)-Npx as a strong chromophore would allow sensitive detection.This work is significant not only in the area of enantioseparation and determination of absolute configuration of diastereomeric derivatives but also in the area of developing new chiral derivatizing reagents (CDRs).

Keywords: chiral derivatizing reagent, naproxen, salbutamol, synthesis

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Authors: L. Gyansah, Yanfang Shen, Jiqiang Wang, Tianying Xiong

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SiCₚ/ pure Al composites with different SiC fractions (20 wt %, 30 wt %, and 40 wt %) were precisely cold sprayed, followed by hot axial-compression tests at deformation temperatures of 473 K to 673 K, leading to failure of specimens through routine crack propagation in their multiphase. The plastic deformation behaviour with respect to the SiCₚ contents and the deformation temperatures were studied at strain rate 1s-1.As-sprayed and post-failure specimens were analyzed by X-ray computed tomography (XCT), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Quasi-static thermomechanical testing results revealed that compressive strength (UTS = 228 MPa and 30.4 %) was the highest in the composites that was thermomechanically compressed at 473 K compared to those of the as-sprayed, while the as-sprayed exhibited a compressive strength of 182.8 MPa related to the increment in SiC fraction. Strength—plasticity synergy was promoted by dynamic recrystallization (DRX) through strengthening and refinement of the grains. The DRX degree depends relevantly on retainment of the uniformly ultrafine SiCₚ particulates, the pinning effects of the interfaces promoted by the ultrafine grain structures (UFG), and the higher deformation temperature. Reconstructed X-ray computed tomography data revealed different crack propagation mechanisms. A single-plane shear crack with multi-laminates fracture morphology yields relatively through the as-sprayed and as-deformed at 473 K deposits, while a multiphase plane shear cracks preeminently existed in high temperature deformed deposits resulting in multiphase-interface delaminations. Three pertinent strengthening mechanisms, videlicet, SiCp dispersed strengthening, refined grain strengthening, and dislocation strengthening, existed in the gradient microstructure, and their detailed contributions to the thermomechanical properties were discussed.

Keywords: cold spraying, hot deformation, deformation temperature, thermomechancal properties, SiC/Al composite

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Authors: Etibar Guliyev, Durdana Jafarli

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The breakdown of the Soviet Union in the early 1990s and the 9/11 attacks resulted in the global changes created a totally new geopolitical situation for the Muslim populated republics of the former Soviet Union. Located between great powers such as China and Russia, as well as theocratic states like Iran and Afghanistan, the newly independent Central Asian states were facing a dilemma to choose a new politico-ideological course for development. Policies dubbed Perestroyka and Glasnost leading to the collapse of the world’s once superpower brought about a considerable rise in the national and religious self-consciousness of the Muslim population of the USSR where the religion was prohibited under the strict communist rule. Moreover, the religious movements prohibited during the Soviet era acted as a part of national straggle to gain their freedom from Moscow. The policies adopted by the Central Asian countries to manage the religious revival and extremism in their countries vary dramatically from each other. As Kazakhstan and Kyrgyzstan are located between Russia and China and hosting a considerable number of the Russian population, these countries treated Islamic revival more tolerantly trying benefit from it in the nation-building process. The importance of the topic could be explained with the fact that it investigates an alternative way of management of religious activities and movements. The recent developments in the Middle East, Syria and Iraq in particular, and the fact that hundreds of fighters from the Central Asian republics joined the ISIL terrorist organization once again highlights the implications of the proper regulation of religious activities not only for domestic, but also for regional and global politics. The paper is based on multiple research methods. The process trace method was exploited to better understand the Russification and anti-religious policies to which the Central Asian countries were subject during the Soviet era. The comparative analyse method was also used to better understand the common and distinct features of the politics of religion of Kazakhstan and Kyrgyzstan and the rest of the Central Asian countries. Various legislation acts, as well as secondary sources were investigated to this end. Mostly constructivist approach and a theory suggesting that religion supports national identity when there is a third cohesion that threatens both and when elements of national identity are weak. Preliminary findings suggest that in line with policies aimed at gradual reduction of Russian influence, as well as in the face of ever-increasing migration from China, the mentioned countries incorporated some Islamic elements into domestic policies as a part and parcel of national culture. Kazakhstan and Kyrgyzstan did not suppress religious activities, which was case in neighboring states, but allowed in a controlled way Islamic movements to have a relatively freedom of action which in turn led to the less violent religious extremism further boosting national identity.

Keywords: identity, Islam, nationalism, terrorism

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Reza Miresmaeili, Asghar Heydari Astaraee, Fereshteh Dolati

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In the present study, an analytical model based on dislocation density model was developed to simulate grain refinement in surface mechanical attrition treatment (SMAT). The correlation between SMAT time and development in plastic strain on one hand, and dislocation density evolution, on the other hand, was established to simulate the grain refinement in SMAT. A dislocation density-based constitutive material law was implemented using VUHARD subroutine. A random sequence of shots is taken into consideration for multiple impacts model using Python programming language by utilizing a random function. The simulation technique was to model each impact in a separate run and then transferring the results of each run as initial conditions for the next run (impact). The developed Finite Element (FE) model of multiple impacts describes the coverage evolution in SMAT. Simulations were run to coverage levels as high as 4500%. It is shown that the coverage implemented in the FE model is equal to the experimental coverage. It is depicted that numerical SMAT coverage parameter is adequately conforming to the well-known Avrami model. Comparison between numerical results and experimental measurements for residual stresses and depth of deformation layers confirms the performance of the established FE model for surface engineering evaluations in SMA treatment. X-ray diffraction (XRD) studies of grain refinement, including resultant grain size and dislocation density, were conducted to validate the established model. The full width at half-maximum in XRD profiles can be used to measure the grain size. Numerical results and experimental measurements of grain refinement illustrate good agreement and show the capability of established FE model to predict the gradient microstructure in SMA treatment.

Keywords: dislocation density, grain refinement, severe plastic deformation, simulation, surface mechanical attrition treatment

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Raed KOUTA, José-Alcebiades-Ernesto HLUNGUANE, Eric Châtele

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The search for and exploitation of new fossil energy resources is taking place in the context of the gradual depletion of existing deposits. Despite the adoption of international targets to combat global warming, the demand for fuels continues to grow, contradicting the movement towards an energy-efficient society. The increase in the share of offshore in global hydrocarbon production tends to compensate for the depletion of terrestrial reserves, thus constituting a major challenge for the players in the sector. Through the economic potential it represents, and the energy independence it provides, offshore exploitation is also a challenge for States such as Mozambique, which have large maritime areas and whose environmental wealth must be considered. The exploitation of new reserves on economically viable terms depends on available technologies. The development of deep and ultra-deep offshore requires significant research and development efforts. Progress has also been made in managing the multiple risks inherent in this activity. Our study proposes a reliability approach to develop products and processes designed to live at sea. Indeed, the context of an offshore platform requires highly reliable solutions to overcome the difficulties of access to the system for regular maintenance and quick repairs and which must resist deterioration and degradation processes. One of the characteristics of failures that we consider is the actual conditions of use that are considered 'extreme.' These conditions depend on time and the interactions between the different causes. These are the two factors that give the degradation process its dynamic character, hence the need to develop dynamic reliability models. Our work highlights mathematical models that can explicitly manage interactions between components and process variables. These models are accompanied by numerical resolution methods that help to structure a dynamic reliability approach in a physical and probabilistic context. The application developed makes it possible to evaluate the reliability, availability, and maintainability of a floating storage and unloading platform for liquefied natural gas production.

Keywords: dynamic reliability, offshore plateform, stochastic process, uncertainties

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Authors: Sarmiza Stanca, Wolfgang Fritzsche, Christoph Krafft, Jürgen Popp

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Independent of the cell type a thin layer of a few nanometers thickness surrounds the cell interior as the cellular membrane. The transport of ions and molecules through the membrane is achieved in a very precise way by pores. Understanding the process of opening and closing the pores due to an electrochemical gradient across the membrane requires knowledge of the pore constitutive proteins. Recent reports prove the access to the molecular level of the cellular membrane by atomic force microscopy (AFM). This technique also permits an electrochemical study in the immediate vicinity of the tip. Specific molecules can be electrochemically localized in the natural cellular membrane. Our work aims to recognize the protein domains of the pores using an AFM probe as a miniaturized amperometric sensor, and to follow the protein behavior while changing the applied potential. The intensity of the current produced between the surface and the AFM probe is amplified and detected simultaneously with the surface imaging. The AFM probe plays the role of the working electrode and the substrate, a conductive glass on which the cells are grown, represent the counter electrode. For a better control of the electric potential on the probe, a third electrode Ag/AgCl wire is mounted in the circuit as a reference electrode. The working potential is applied between the electrodes with a programmable source and the current intensity in the circuit is recorded with a multimeter. The applied potential considers the overpotential at the electrode surface and the potential drop due to the current flow through the system. The reported method permits a high resolved electrochemical study of the protein domains on the living cell membrane. The amperometric map identifies areas of different current intensities on the pore depending on the applied potential. The reproducibility of this method is limited by the tip shape, the uncontrollable capacitance, which occurs at the apex and a potential local charge separation.

Keywords: AFM, sensor, membrane, pores, proteins

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Authors: K. Sridhar Reddy, M. Amaranatha Reddy, Nilanjan Mitra

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Short paneled concrete pavement (SPCP) with shorter panel size can be an alternative to the conventional jointed plain concrete pavements (JPCP) at the same cost as the asphalt pavements with all the advantages of concrete pavement with reduced thickness, less chance of mid-slab cracking and or dowel bar locking so common in JPCP. Cast-in-situ short concrete panels (short slabs) laid on a strong foundation consisting of a dry lean concrete base (DLC), and cement treated subbase (CTSB) will reduce the thickness of the concrete slab to the order of 180 mm to 220 mm, whereas JPCP was with 280 mm for the same traffic. During the construction of SPCP test sections on two Indian National Highways (NH), it was observed that the joints remain uncracked after a year of traffic. The undoweled and uncracked joints load transfer variability and joint behavior are of interest with anticipation on its long-term performance of the SPCP. To investigate the effects of undoweled and uncracked joints on short slabs, the present study was conducted. A multilayer linear elastic analysis using 3D finite element package for different panel sizes with different thicknesses resting on different types of solid elastic foundation with and without temperature gradient was developed. Surface deflections were obtained from 3D FE model and validated with measured field deflections from falling weight deflectometer (FWD) test. Stress analysis indicates that flexural stresses in short slabs are decreased with a decrease in panel size and increase in thickness. Detailed evaluation of stress analysis with the effects of curling behavior, the stiffness of the base layer and a variable degree of load transfer, is underway.

Keywords: joint behavior, short slabs, uncracked joints, undoweled joints, 3D numerical simulation

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Authors: Lina Jassim, Robiah Yunus, , Amran Salleh

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Since nano particles have been assessed as thermo stabilizer, rheology enhancer, and ecology safer, nano drilling fluid can be utilized to overcome the complexity of hole cleaning in highly deviated interval of an extended reach wells. The eccentric annular flow is a flow with special considerations; it forms a vital part of drilling fluid flow analysis in an extended reach wells. In this work eccentric, dual phase flow (different types of rock cuttings with different size were blended with nano fluid) through horizontal well (an extended reach well) are simulated with the help of CFD, Fluent package. In horizontal wells flow occurs in an adverse pressure gradient condition, that makes the particle inside it susceptible to reversed flow. Thus the flow has to be analyzed in a three dimensional manner. Moreover the non-Newtonian behavior of the nano fluid makes the problem really challenging in numerical and physical aspects. The primary objective of the work is to establish a relationship between different flow characteristics with the speed of inner wall rotation. The nano fluid flow characteristics include swirl of flow and its effect on wellbore cleaning ability , wall shear stress and its effect on fluid viscosity to suspend and carry the rock cuttings, axial velocity and its effect on transportation of rock cuttings to the wellbore surface, finally pressure drop and its effect on managed of drilling pressure. The importance of eccentricity of the inner cylinder has to be analyzed as a part of it. Practical horizontal well flows contain a good amount of particles (rock cuttings) with moderate axial velocity, which verified nano drilling fluid ability of carrying and transferring cuttings particles in the highly deviated eccentric annular flow is also of utmost importance.

Keywords: Non-Newtonian, dual phase, eccentric annular, CFD

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Authors: Sebastien Relave, Christophe Desrayaud, Aurelien Vilani, Alexey Sova

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Laser beam melting (LBM) is an additive manufacturing process that enables complex 3D parts to be designed. This process is now commonly employed for various applications such as chemistry or energy, requiring the use of stainless steel grades. LBM can offer comparable and sometimes superior mechanical properties to those of wrought materials. However, we observed an anisotropic microstructure which results from the process, caused by the very high thermal gradients along the building axis. This microstructure can be harmful depending on the application. For this reason, control and prediction of the microstructure are important to ensure the improvement and reproducibility of the mechanical properties. This study is focused on the 316L SS grade and aims at understanding the solidification and transformation mechanisms during process. Experiments to analyse the nucleation and growth of the microstructure obtained by the LBM process according to several conditions. These samples have been designed on different type of support bulk and lattice. Samples are produced on ProX DMP 200 LBM device. For the two conditions the analysis of microstructures, thanks to SEM and EBSD, revealed a single phase Austenite with preferential crystallite growth along the (100) plane. The microstructure was presented a hierarchical structure consisting columnar grains sizes in the range of 20-100 µm and sub grains structure of size 0.5 μm. These sub-grains were found in different shapes (columnar and cellular). This difference can be explained by a variation of the thermal gradient and cooling rate or element segregation while no sign of element segregation was found at the sub-grain boundaries. A high dislocation concentration was observed at sub-grain boundaries. These sub-grains are separated by very low misorientation walls ( < 2°) this causes a lattice of curvature inside large grain. A discussion is proposed on the occurrence of these microstructures formation, in regard of the LBM process conditions.

Keywords: selective laser melting, stainless steel, microstructure

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Authors: Chukwudalu C. Nwazojie, Wole W. Soboyejo, John Obayemi, Ali Salifu Azeko, Sandra M. Jusu, Chinyerem M. Onyekanne

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The conventional methods for the diagnosis and treatment of breast cancer include bulk systematic mammography, ultrasound, dynamic contrast-enhanced fast 3D gradient-echo (GRE) magnetic resonance imaging (MRI), surgery, chemotherapy, and radiotherapy. However, nanoparticles and drug-loaded polymer microspheres for disease (cancer) targeting and treatment have enormous potential to enhance the approaches that are used today. The goal is to produce an implantable biomedical device for localized breast cancer drug delivery within Africa and the world. The main advantage of localized delivery is that it reduces the amount of drug that is needed to have a therapeutic effect. Polymer blends of poly (D,L-lactide-co-glycolide) (PLGA) and polycaprolactone (PCL), which are biodegradable, is used as a drug excipient. This work focuses on the development of PLGA-PCL (poly (D,L-lactide-co-glycolide) (PLGA) blended with based injectable drug microspheres and are loaded with anticancer drugs (prodigiosin (PG), and paclitaxel (PTX) control) and also the conjugated forms of the drug functionalized with LHRH (luteinizing hormone-releasing hormone) (PG-LHRH, and PTX- LHRH control), using a single-emulsion solvent evaporation technique. The encapsulation was done in the presence of PLGA-PCL (as a polymer matrix) and poly-(vinyl alcohol) (PVA) (as an emulsifier). Comparative study of the various drugs release kinetics and degradation mechanisms of the PLGA-PCL with an encapsulated drug is achieved, and the implication of this study is for the potential application of prodigiosin PLGA-PCL loaded microparticles for controlled delivery of cancer drug and treatment to prevent the regrowth or locoregional recurrence, following surgical resection of triple-negative breast tumor.

Keywords: cancer, polymers, drug kinetics, nanoparticles

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Authors: Nahia H. Sassine, Frédéric-Victor Donzé, Arnaud Bruch, Barthélemy Harthong

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Thermal Energy Storage (TES) systems are central elements of various types of power plants operated using renewable energy sources. Packed bed TES can be considered as a cost–effective solution in concentrated solar power plants (CSP). Such a device is made up of a tank filled with a granular bed through which heat-transfer fluid circulates. However, in such devices, the tank might be subjected to catastrophic failure induced by a mechanical phenomenon known as thermal ratcheting. Thermal stresses are accumulated during cycles of loading and unloading until the failure happens. For instance, when rocks are used as storage material, the tank wall expands more than the solid medium during charge process, a gap is created between the rocks and tank walls and the filler material settles down to fill it. During discharge, the tank contracts against the bed, resulting in thermal stresses that may exceed the wall tank yield stress and generate plastic deformation. This phenomenon is repeated over the cycles and the tank will be slowly ratcheted outward until it fails. This paper aims at studying the evolution of tank wall stresses over granular bed thermal cycles, taking into account both thermal and mechanical loads, with a numerical model based on the discrete element method (DEM). Simulations were performed to study two different thermal configurations: (i) the tank is heated homogeneously along its height or (ii) with a vertical gradient of temperature. Then, the resulting loading stresses applied on the tank are compared as well the response of the internal granular material. Besides the study of the influence of different thermal configurations on the storage tank response, other parameters are varied, such as the internal angle of friction of the granular material, the dispersion of particles diameters as well as the tank’s dimensions. Then, their influences on the kinematics of the granular bed submitted to thermal cycles are highlighted.

Keywords: discrete element method (DEM), thermal cycles, thermal energy storage, thermocline

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Authors: Yunhui Sun, Qingquan Liu, Xiaoliang Wang

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Dense granular flows widely exist in geological flows such as debris flow, landslide, or sheet flow, where both the interparticle and solid-liquid interactions are important to modify the flow. So, a two-phase approach with both phases correctly modelled is important for a better investigation of the saturated granular flows. However, a proper closure model covering a wide range of flowing states for the solid phase is still lacking. This study first employs a chute flow experiment based on the refractive index matching method, which makes it possible to obtain internal flow information such as velocity, shear rate, granular fluctuation, and volume fraction. The granular stress is obtained based on a steady assumption. The kinetic theory is found to describe the stress dependence on the flow state well. More importantly, the granular rheology is found to be frictionally dominated under weak shear and collisionally dominated under strong shear. The results presented thus provide direct experimental evidence on a possible frictional-collisional closure model for the granular phase. The data indicates that both frictional stresses exist over a wide range of the volume fraction, though traditional theory believes it vanishes below a critical volume fraction. Based on the findings, a two-phase model is used to simulate the chute flow. Both phases are modelled as continuum media, and the inter-phase interactions, such as drag force and pressure gradient force, are considered. The frictional-collisional model is used for the closure of the solid phase stress. The profiles of the kinematic properties agree well with the experiments. This model is further used to simulate immersed granular collapse, which is unsteady in nature, to study the applicability of this model, which is derived from steady flow.

Keywords: closure model, collision, friction, granular flow, two-phase model

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Authors: Ina Nasto, Denada Sota, Pudrila Haskoçelaj, Mariola Ismailaj, Hajdar Kicaj

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Most of the fishing areas of the Mediterranean Sea are considered to be overfished, consequently fishing has decreased or is static. Considering the continuous increase in demand for fish, the option of aquaculture production has had a growing development in recent decades. The environmental impact of aquaculture in the marine ecosystem has been a subject of study for several years in the Mediterranean. In the case of the Albanian waters, and in particular the Gulf of Vlora, have had a progressive growing of aquaculture activity in the last twenty years. Given the convenient and secluded location for tourist activities, the bay of Ragusa was considered as the most suitable area to install the aquaculture cage system for the breeding of sea bass and sea bream. The impact of aquaculture in on the soft bottom benthic communities has been assessed at the biggest commercial fish farm (Alb-Adriatico Sh.P.K) established in coastal waters of Ragusa bay 30–50 m deep, in the southern part of the Gulf of Vlora. In order to determine if there is a possible impact on the aquaculture cage in benthic communities, a comparative analysis was undertaken between transects and samples with differences in distances between them and with a gradient of distance from the fish cages. A total of 275 taxa were identified (1 Foraminifera, 1 Porifera, 3 Cnidaria, 2 Platyhelminthes, 2 Nemertea, 1 Bryozoa, 171 Mollusca, 39 Annelida, 35 Crustacea, 14 Echinodermata, 1 Hemichordata, and 5 Tunicata). The anaysis showed three main habitats in the area: biocoenosis of terrigenous mud, residual areas with Possidonia oceanica and also residual assemblages of algal coralligenous. Four benthic biotic indexes were calculated (Shannon H ’, BENTIX, Simpson's Diversity and Peilou’s J’) also benthic indicators as total abundance, number of taxa and species frequency to evaluate possible ecological impact of fish cages in Ragusa bay.

Keywords: Bentix index, Benthic community, invertebrates, aquaculture, Raguza bay

Procedia PDF Downloads 67